[bar-spine] Spine Plot for Two-Variable Proportions

[MarkusNeusinger]

Description

A spine plot (also called a spineplot) is a stacked bar chart where bar widths are proportional to the marginal frequency of one variable and the subdivisions within each bar show the conditional distribution of a second variable. It is a 1D specialization of mosaic plots and excels at visualizing the relationship between two categorical variables.

Applications

• Showing survival rates across passenger classes (Titanic dataset)
• Visualizing disease prevalence across age groups
• Displaying customer churn rates by subscription tier
• Comparing pass/fail rates across different test conditions

Data

• x_category (str) — primary categorical variable (determines bar width)
• fill_category (str) — secondary variable (stacked segments within bars)
• count (int) — frequency for each combination
• Size: 3–8 x-categories, 2–5 fill categories

Notes

• Bar width proportional to marginal count of x_category
• Segments within each bar show conditional proportions
• All bars same height (normalized to 100%)
• Complements the existing mosaic-categorical spec

[claude]

✅ Spec ID: bar-spine

Rationale: A spine plot is a variable-width stacked bar chart normalized to 100% height, making bar-spine a natural fit within the existing bar chart family. It is related to mosaic-categorical (2D mosaic) and marimekko-basic (value-weighted areas), but distinct in its focus on conditional proportions from count-based contingency data with uniform 100% bar height.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: bar-spine

PR: #5266

---

specification.md

bar-spine: Spine Plot for Two-Variable Proportions

Description

A spine plot (spineplot) is a stacked bar chart where bar widths are proportional to the marginal frequency of one categorical variable and the subdivisions within each bar show the conditional distribution of a second categorical variable. All bars are normalized to the same height (100%), so visual comparison focuses on how the conditional proportions shift across categories. It is a one-dimensional specialization of mosaic plots and excels at revealing associations between two categorical variables in contingency table data.

Applications

• Showing survival rates across passenger classes in the Titanic dataset
• Visualizing disease prevalence across demographic age groups
• Displaying customer churn rates segmented by subscription tier
• Comparing pass/fail rates across different experimental test conditions

Data

• x_category (categorical) - Primary categorical variable that determines bar width (marginal frequency)
• fill_category (categorical) - Secondary categorical variable shown as stacked segments within each bar
• count (integer) - Frequency count for each combination of x_category and fill_category
• Size: 3-8 x-categories, 2-5 fill categories for readability
• Example: Titanic survival data with passenger class as x_category, survival status as fill_category, and passenger counts

Notes

• Bar width must be proportional to the marginal count of each x_category
• Segments within each bar represent conditional proportions of fill_category (heights sum to 100%)
• All bars share the same total height (normalized to 100%)
• Bars should be adjacent with no gaps to emphasize the continuous proportion axis
• Use distinct colors for each fill_category with a clear legend
• X-axis labels should be centered under each variable-width bar
• Consider adding percentage labels within segments when space permits
• Complements the existing mosaic-categorical spec as a simpler 1D alternative

---

Next: Add approved label to merge this specification to main.

---

🤖 spec-create workflow

[github-actions]

🚀 Specification Merged!

Specification: bar-spine
PR: #5266 (merged)

The specification is now in main and synced to PostgreSQL.

---

Next: Add generate:{library} labels to start generating implementations.

Available libraries: matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, letsplot

Or use workflow dispatch for bulk operations.

---

🤖 spec-create workflow